Stanford HAI

Bridging Recommendation Algorithm Simulator

Research Principle: “Our feeds shouldn’t value confrontational engagement over complementary content, nor should they obsequiously feed us only content we agree with. They should strike a balance and mend social disagreements rather than make them worse.” — Michael Bernstein, Stanford HAI Senior Fellow

1. Algorithm Objective Tuner Balanced Complementary Bridging

Bridging / Cross-Group Consensus 0.50

Rewards posts liked by diverse ideological clusters rather than single factions.

Ideological Affinity (Homophily) 0.35

Prioritizes in-group agreement and familiar network confirmation.

Confrontational Outrage Engagement 0.15

Boosts high-velocity replies, heated quotes, and viral disagreement.

Polarization Index
0.28
Low Division
Cross-Ideology Exposure
64.5%
Healthy Exchange
Outrage Amplification
0.32
Damped Hostility
Community Health Score
88/100
Constructive
Simulated Network Graph 3 Clusters • 12 Bridge Posts
Cluster A
Cluster B
Bridge Post

2. Live Curation Feed Stream

Displaying top 12 posts scored by active multi-objective model
Real-Time Ranking Active
Select a post above to inspect ranking weight breakdown
Click on any post in the feed to see how bridging consensus, ideological affinity, and outrage dynamics calculate its exact position.

3. Stanford HAI Algorithmic Policy Brief Export

Durable summary of simulated objective weights, exposure telemetry, and mitigation insights.


    


    
  


  


Enjoy this tool? Build your own with Super